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The role of decision tree representation in regression problems – An evolutionary perspective

Marcin Czajkowski, Marek Kretowski
2016 Applied Soft Computing  
As it is difficult if not impossible to choose the best representation for a particular problem in advance, the issue is investigated using a new evolutionary algorithm for the decision tree induction  ...  A regression tree is a type of decision tree that can be applied to solve regression problems.  ...  We also proposed a unified framework for the global induction of mixed trees that may have both univariate and oblique tests in internal nodes and regression planes and models in leaves.  ... 
doi:10.1016/j.asoc.2016.07.007 fatcat:2o6f4ywgtrf6pcmipqju35xyiy

Special issue: Advances in learning schemes for function approximation

Emilio Corchado, Ajith Abraham, Pedro Antonio Gutiérrez, José Manuel Benítez, Sebastián Ventura
2014 Neurocomputing  
This work was also supported in the framework of the IT4 Innovations Centre of Excellence Project, Reg. no.  ...  CZ.1.05/1.1.00/02.0070 by operational program 'Research and Development for Innovations 0 funded by the Structural Funds of the European Union and state budget of the Czech Republic, EU.  ...  In the first contribution, Barros et al., propose a novel Bottom-Up Oblique Decision-Tree Induction Framework called BUTIF.  ... 
doi:10.1016/j.neucom.2013.12.003 fatcat:ikpihxcnnfd5jjryupu64jwn5q

Neural Decision Trees [article]

Randall Balestriero
2017 arXiv   pre-print
In this paper we propose a synergistic melting of neural networks and decision trees (DT) we call neural decision trees (NDT).  ...  NDT is an architecture a la decision tree where each splitting node is an independent multilayer perceptron allowing oblique decision functions or arbritrary nonlinear decision function if more than one  ...  As a result, oblique decision trees have been developed for which a test is now done on a linear combination of the attributes of x.  ... 
arXiv:1702.07360v2 fatcat:c2vrx7ux5vge5n5je5jkuxmhom

A Survey on Issues of Decision Tree and Non-Decision Tree Algorithms

Kishor Kumar Reddy C, Vijaya Babu
2016 International Journal of Artificial Intelligence and Applications for Smart Devices  
Decision tree learning from a very huge set of records in a database is quite complex task and is usually a very slow process, which is often beyond the capabilities of existing computers.  ...  This paper is an attempt to summarize the proposed approaches, tools etc. for decision tree learning with emphasis on optimization of constructed trees and handling large datasets.  ...  A method for pruning of oblique decision trees was proposed by Shah and Sastry [37] .  ... 
doi:10.14257/ijaiasd.2016.4.1.02 fatcat:atql2wwaffej3czppm3o444jqm

Optimization of Hierarchical Regression Model with Application to Optimizing Multi-Response Regression K-ary Trees

Pooya Tavallali, Peyman Tavallali, Mukesh Singhal
2019 PROCEEDINGS OF THE THIRTIETH AAAI CONFERENCE ON ARTIFICIAL INTELLIGENCE AND THE TWENTY-EIGHTH INNOVATIVE APPLICATIONS OF ARTIFICIAL INTELLIGENCE CONFERENCE  
A fast, convenient and well-known way toward regression is to induce and prune a binary tree. However, there has been little attempt toward improving the performance of an induced regression tree.  ...  This paper presents a meta-algorithm capable of minimizing the regression loss function, thus, improving the accuracy of any given hierarchical model, such as k-ary regression trees.  ...  Acknowledgment Peyman Tavallali's research contribution to this paper was carried out at the Jet Propulsion Laboratory, California Institute of Technology, under a contract with the National Aeronautics  ... 
doi:10.1609/aaai.v33i01.33015133 fatcat:img4lmgugbdbjjgp5ptrfdwyty

Automated Discovery of Polynomials by Inductive Genetic Programming [chapter]

Nikolay Nikolaev, Hitoshi Iba
1999 Lecture Notes in Computer Science  
Evolutionary search is used for learning polynomials represented as non-linear multivariate trees. Optimal search performance is pursued with balancing the statistical bias and the variance of iGP.  ...  This paper presents an approach to automated discovery of high-order multivariate polynomials by inductive Genetic Programming (iGP).  ...  The two iGP systems are related to a knowledge discovery system Ltree [3] that uses oblique decision trees, since it also finds non-linear approximations of the data, and to the decision tree learning  ... 
doi:10.1007/978-3-540-48247-5_58 fatcat:rkexjq73ffcfnbqgkmrexgvgyu

Memory optimisation for hardware induction of axis-parallel decision tree

Chuan Cheng, Christos-Savvas Bouganis
2014 2014 International Conference on ReConFigurable Computing and FPGAs (ReConFig14)  
The induction of Decision Trees (i.e. training stage) involves intense memory communication and inherent parallel processing, making an FPGA device a promising platform for accelerating the training process  ...  In data mining and machine learning applications, the Decision Tree classifier is widely used as a supervised learning method not only in the form of a stand alone model but also as a part of an ensemble  ...  The induction of a decision tree involves consecutive splittings of a training dataset in the feature space.  ... 
doi:10.1109/reconfig.2014.7032538 dblp:conf/reconfig/ChengB14 fatcat:4a2bpmehvnhonguoaolldotvme

Functional Trees

João Gama
2004 Machine Learning  
This framework combines a univariate decision tree with a linear function by means of constructive induction.  ...  In order to study the use of functional nodes at different places and for different types of modeling, we introduce a simple unifying framework for multivariate tree learning.  ...  Acknowledgments Thanks to the detailed comments of the editor and the anonymous reviewers that much improved the text.  ... 
doi:10.1023/b:mach.0000027782.67192.13 fatcat:2docnsl24bdprcehlxzr4d3bzm

Multi-GPU approach to global induction of classification trees for large-scale data mining

Krzysztof Jurczuk, Marcin Czajkowski, Marek Kretowski
2021 Applied intelligence (Boston)  
AbstractThis paper concerns the evolutionary induction of decision trees (DT) for large-scale data. Such a global approach is one of the alternatives to the top-down inducers.  ...  The searches for the tree structure and tests are performed simultaneously on a CPU, while the fitness calculations are delegated to GPUs.  ...  In the CUDT system [41] , the induction time of a typical decision tree was reduced up to 55 times.  ... 
doi:10.1007/s10489-020-01952-5 fatcat:nv2pudcn5jafldo2jodrjdkode

Applying machine learning to agricultural data

Robert J. McQueen, Stephen R. Garner, Craig G. Nevill-Manning, Ian H. Witten
1995 Computers and Electronics in Agriculture  
We briefly survey some of the techniques emerging from machine learning research, describe a software workbench for experimenting with a variety of techniques on real-world data sets, and describe a case  ...  study of dairy herd management in which culling rules were inferred from a medium-sized database of herd information.  ...  Acknowledgements This work is supported by the New Zealand Foundation for Research, Science and Technology.  ... 
doi:10.1016/0168-1699(95)98601-9 fatcat:tr4ayqfa7jfqbjvozpeenm3svu

Sparse Projection Oblique Randomer Forests [article]

Tyler M. Tomita, James Browne, Cencheng Shen, Jaewon Chung, Jesse L. Patsolic, Benjamin Falk, Jason Yim, Carey E. Priebe, Randal Burns, Mauro Maggioni, Joshua T. Vogelstein
2019 arXiv   pre-print
Decision forests, including Random Forests and Gradient Boosting Trees, have recently demonstrated state-of-the-art performance in a variety of machine learning settings.  ...  Decision forests are typically ensembles of axis-aligned decision trees; that is, trees that split only along feature dimensions.  ...  The result of the tree induction algorithm is a set of split nodes and leaf nodes.  ... 
arXiv:1506.03410v6 fatcat:4b6eswjkpbcy3h3nkzyd257msq

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M.A. Maloof, P. Langley, T.O. Binford, R. Nevatia, S. Sage
2012 Machine Learning  
In this paper, we examine the use of machine learning to improve a rooftop detection process, one step in a vision system that recognizes buildings in overhead imagery.  ...  They demonstrate that under most conditions, naive Bayes exceeded the accuracy of other methods and a handcrafted classifier, the solution currently used in the building detection system.  ...  Acknowledgments The authors thank Wayne Iba for assistance with naive Bayes, Dan Shapiro for discussions about decision theory, Yan Bulgak for proofreading a draft of the paper, Ross Quinlan for describing  ... 
doi:10.1023/a:1025623527461 fatcat:4hyidiiwmzdqtintyrqgsdrdvi

Co-location decision tree for enhancing decision-making of pavement maintenance and rehabilitation

Guoqing Zhou, Linbing Wang
2012 Transportation Research Part C: Emerging Technologies  
This dissertation presents the development of theory and algorithm for a new decision tree induction method, called co-location-based decision tree (CL-DT.)  ...  from 61.2% to 9.7%, which implies that the training data can contribute to decision tree induction; (3) the proposed CL-DT algorithm saves the 20% computational time taken in tree growing, tree drawing  ...  Decision Rule Induction Decision rules are directly induced by translating a decision tree either in a bottom-up specificto-general style, or in a top-down general-to-specific style (Aptė and Weiss, 1997  ... 
doi:10.1016/j.trc.2011.10.007 fatcat:t4uahzgyfbdtxmmpr67ylwq3pq

Evolutionary Design of Decision-Tree Algorithms Tailored to Microarray Gene Expression Data Sets

Rodrigo C. Barros, Marcio P. Basgalupp, Alex A. Freitas, Andre C. P. L. F. de Carvalho
2014 IEEE Transactions on Evolutionary Computation  
In this paper, we propose a paradigm shift in the research of decision trees: instead of proposing a new manually designed method for inducing decision trees, we propose automatically designing decision-tree  ...  By the end of the evolution, we expect HEAD-DT to generate a new and possibly better decision-tree algorithm for a given application domain.  ...  Many different types of heuristic procedures have been proposed in the literature for building decision trees, such as hill-climbing bottom-up induction [8] , hybrid induction [9] , evolutionary induction  ... 
doi:10.1109/tevc.2013.2291813 fatcat:pc3ztjy3szeclii5565jljb63m

Dive into Decision Trees and Forests: A Theoretical Demonstration [article]

Jinxiong Zhang
2021 arXiv   pre-print
While decision trees have a long history, recent advances have greatly improved their performance in computational advertising, recommender system, information retrieval, etc.  ...  In simple words, decision trees use the strategy of "divide-and-conquer" to divide the complex problem on the dependency between input features and labels into smaller ones.  ...  And we can induce the sparse weighted oblique decision trees as shown in SWOT. Another aim of sparse decision trees is to regularize with sparsity for interpretability.  ... 
arXiv:2101.08656v1 fatcat:lksqf44i7fh2jfnqlvdspddzba
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